Although magnetocardiography (MCG) was introduced in the early 1960's as a possible diagnostic tool, it took almost thirty years to successfully demonstrate its clinical value. Today, it represents one of the emerging new technologies in cardiology employed by physicians in hospitals around the world. The clinical application of MCG method has significantly benefited from modern multichannel sensor technology, sophisticated software, as well as from recent improvements in hardware allowing the use of the device without a magnetically-shielded room.
MCG studies are fast, safe and completely noninvasive. Consequently, this offers great convenience for the patient. Currently, many groups work on establishing libraries of reference data and data standardization. There are several clinical applications for which MCG has already provided clinically-useful results. For example, MCG can diagnose and localize acute myocardial infarctions, separate myocardial infarction patients with and without susceptibility of malignant ventricular arrhythmias, detect ventricular hypertrophy and rejection after heart transplant, localize the site of ventricular pre-excitation and many types of cardiac arrhythmias, as well as reveal fetal arrhythmias and conduction disturbances [40]. In addition, several other clinical applications of MCG have recently been studied: detection and risk stratification of cardiomyopathies (dilated, hypertrophic, arrhythmogenic, diabetic), risk stratification after idiopathic ventricular fibrillation, detection and localization of myocardial viability, and follow-up of fetal growth and neural integrity. Some studies have clearly indicated that MCG is very sensitive to changes in repolarization, e.g., after myocardial infarction or in a hereditary long-QT syndrome [42]. The most relevant overview of MCG applications and currently-used analysis techniques can be found in [41].
An important challenge, however, is to reduce or eliminate the variability introduced by human interpretation of MCG data, and to significantly improve the machine-based classification performance and quality of generalization, while maintaining computer processing times that are compatible with real-time diagnosis.
Three basic steps are always performed when applying artificial intelligence (machine learning) to measured data: 1. measurement of the data, 2. pre-processing of the measured data, 3. training of the adaptive classifier. Patents incorporating this basic approach to EKG/ECG data or other biological data include U.S. Pat. Nos. 5,092,343; 5,280,792; 5,465,308; 5,680,866; 5,819,007; 6,128,608; 6,248,063; 6,443,889; 6,572,560; 6,714,925; and 6,728,691.
The use of artificial intelligence for analysis of MCG field patterns is quite limited to date. One reference for the application of artificial intelligence for analysis of biomagnetic signals is U.S. Pat. No. 5,417,211, which discloses a method for classifying field patterns generated by electrophysiological activities occurring inside the body of a living subject including the steps of measuring field patterns arising as a result of the electrophysiological activities outside the body of the subject using a multi-channel measuring apparatus, generating feature vectors corresponding to the measured field patterns, supplying the feature vectors to an adaptive classifier, and training the adaptive classifier with training field patterns which have been generated by a localizable surrogate model of the electrophysiological activity. The method includes the further step of generating a probability value for each field pattern at an output of the adaptive classifier which indicates the probability with which each field pattern can be generated by a selected localizable surrogate model. Like the EKG/ECG references cited above, this discusses the general applicability of machine learning to the measured data, but does not present the specifics of how to improve the classification performance and quality of generalization.
In all cases, the two key measures which determine success are the classification performance and quality of generalization. While training on non-optimally pre-processed data leads to poor classification results, so-called overtraining prevents the adaptive classifier from generalizing to the proper recognition of real-world data.
The key to success lies in an optimal pre-processing of the data, which has not yet been achieved to date by any of the references cited herein. It is critically important to identify all features that determine the class to which the investigated dataset belongs. It is neither obvious nor trivial to identify those features. Moreover, these features may vary from biological system to biological system, and from one type of measured data to another. In consequence, most artificial intelligence based procedures differ in how the pre-processing is performed.
As will be disclosed in detail herein, the use of kernel transforms and wavelet transforms to preprocess data for machine learning provides the basis for a successful machine learning approach which significantly improves on the prior art in terms of accurate classification, quality of generalization, and speed of processing. This has not been disclosed or suggested in any of the prior art cited herein.